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Inspired by T Smith idea to implement Gary's Antonacci's dual momentum approach to ETF
selection in "IN OUT" strategy.
-The execution code has been completely changed to keep levarage under control and avoid
insufficient buying power warnings.
-To calculate returns I used widely used in industry momentum with excluding period.
-Modified components that are more in line with the strategy.
-The IN OUT part of the strategy has not changed except for some cosmetics
to make it more readable for myself.
"DUAL MOMENTUM IN OUT" nearly doubled "IN OUT" Net Profit while maintaining risk metrics at the same level.
Compounding Annual Return
30.164%
Sharpe Ratio
1.667
PSR
97.773%
Beta
0.057
Drawdown
19.300%
Annual Standard Deviation
0.154
Here is my second version of "DUAL MOMENTUM-IN OUT".
Carsten
Hi Vladimir
good to see you again here...just some off topic question...i arrived as well from quantopian and try to translate/learn porting an algo from zipline. I did the tutorial and the framework they are using looks far better than quantopian but the warming up and other stuff is a bit nerve ripping...
Do you thnik it would be difficult to move that algo aboue into the quantopian framework concept?
# Universe Model self.SetUniverseSelection(MyUniverseSelectionModel()) # Alpha Model self.SetAlpha(MOMAlphaModel()) # PortfolioConstruction Model self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel()) # RiskManagement Model self.SetRiskManagement(MaximumDrawdownPercentPerSecurity(1)) # Execution Model self.SetExecution(ImmediateExecutionModel())I translated already an algo with pre-set ticker, filtering and ranking, if you are intersted i dont mind to post it here. It still has some issus with warm up.
For me alone its quite hard, but I know that you are a very experienced programmer..
Moving to the framework it might a lot easyer to contribute as one only need to improve some modules and don't need to touch the whole code, and one can reuse a lot from other algos.
Vladimir
Hi, Carsten,
For zipline, you can skip warm-up and consolidation because they are built-in features.
Vladimir
Hi Carsten,
if you are intersted i dont mind to post it here
I have created thread Transition from Quantopian to QuantConnect.
You can post it there.
T Smith
Great work. Adjusting the tradeout scheduling and using hourly rather than daily resolution for our signal instruments, I've managed to reduce drawdown.
Vladimir
T Smith
Thank for the improvement.
"Many a little makes a mickle".
There was a typo in the code on line 154.
I deleted the second "if".
Vovik
In this backtest I used the original T Smith setup:
IN and OUT assets determined by momentum (IN: QQQ/IWF, OUT: TLT/IEF), 100 day simple return
Net Profit 2097.037%; Sharpe Ratio 1.743; PSR 97.247%; Drawdown 17.900%; Beta 0.026;
It also sufficiently beat the setup of Peter Guenther.
IN: QQQ:1, OUT: TLT:0.5 + IEF:0.5
Great job Vladimir and T Smith.
Nathan Swenson
Hello Vovik,
Did you fix the issue with the original T.Smith algo? It has many error in the log with invalid trades due to margin violation. I believe Vladimir's has fixed those issue, but trades different instruments.
T Smith
Yeah, Nathan Swenson he is using Vlads corrected trade logic to ensure sells are placed before buys and leverage doesn't go beyond 1. He is also using the updated sheduling and resolutions. This gets us out the market quicker, but avoids over trading/rebalancing whilst 'IN'.
Vovik
Nathan Swenson,
In this backtest I have no margin violation warnings.
Here is the full log.
Algorithm Initializa: tion: Data for symbol DBB has been limited due to numerical precision issues in the factor file. The starting date has been set to 1/4/2007.
Algorithm Initializa: tion: Data for symbol UUP has been limited due to numerical precision issues in the factor file. The starting date has been set to 2/19/2007.
Algorithm Initializa: tion: Data for symbol DBB has been limited due to numerical precision issues in the factor file. The starting date has been set to 1/4/2007.
Algorithm Initializa: tion: Data for symbol UUP has been limited due to numerical precision issues in the factor file. The starting date has been set to 2/19/2007.
Algorithm Initializa: tion: Warning: when performing history requests, the start date will be adjusted if it is before the first known date for the symbol.
2008-01-01 00:00:00 : Launching analysis for b3e5c2897bc5099bb64e69aa0ecedcca with LEAN Engine v2.4.0.0.10016
2007-08-28 10:00:00 : Algorithm warming up...
2008-01-01 00:00:00 : Algorithm finished warming up.
2020-12-11 16:00:00 : Algorithm Id:(b3e5c2897bc5099bb64e69aa0ecedcca) completed in 631.42 seconds at 3k data points per second. Processing total of 2,197,238 data points.
Perhaps the problem is something else.
T Smith
Vladimir regarding your comment on using more than one indicator to trigger the out of market. I have had a go at implementing it here:
Would appreciate your feedback!
Nathan Swenson this seems to solve the issue of SHY taking us out the market without another signal.
Goldie Yalamanchi
That's really great T Smith I am not a huge fan of FDN or QQQ but this strat looks great! Quick question...
Are these the only changes to code to change if you don't want 5 signals as the IN/OUT? Say 4 or 3 for example...
pctl = np.nanpercentile(mom, 5, axis=0)
if self.no_signals > 5:
EllaHamilton
a great algorithm, we will test it on a large scale, and then leave you a review.
Guy Fleury
This strategy, like its predecessors in the other 2 or 3 “...In Out...” threads, is totally dependent on its up/down trend declarations. And the first question to ask should be: How reliable is this trend definition?
I see the strategy as a variant to a 60-day over a 252-day moving average crossover to bond switcher, but still responding to daily price variations. The advantage goes to being long for longer periods than being in bonds. And this translates - in an upmarket - to taking a higher average percent profit than when it loses a trade (about 2:1). And there, even with a hit rate in the 50s, you are bound to make some overall profits just for playing the game.
The above chart illustrates where I'm at presently. There was some leveraging (1.4x). Not excessive when compared to the 89.86% CAGR. The strategy could more than afford to pay the leveraging fees. 3x-leveraged ETFs were used which made the strategy behave the same as if leveraged by 4.2x. The initial capital was set at 100k as most here. Increasing the initial capital to 1 million did increase performance about 10-fold since the strategy is fully scalable.
To get there, I modified a few things. One of interest might be the following section:
mom['S_G'] = (mom[self.SLV] - mom[self.GLD])*0.0 mom['I_U'] = (mom[self.XLI] - mom[self.XLU])*0.0 mom['A_F'] = (mom[self.FXA] - mom[self.FXF])*0.0which flatlined most of the momentum signals leaving only UUP. Why do this? It resulted in higher profits. It also said that those signal components were not needed or could be considered as irrelevant to the task at hand. There is a lot that could be said about this strategy's strengths and weaknesses.
Hannes R
Hello Vladimir and all,
I see your many contributions and I am still a complete newbie, thank you for sharing all these invaluable insights and your experience online for everyone to learn from and contribute too (me too hopefully, one day).
I will need to spend several hours to understand your code and the logic behind , (would be great if there were comments in the code explaining the logic =D) but have some question around lookback bias in the meantime. This strategy is almost too good to be true - what are some of the items in here that may have lead to over-optimization? I see Guy Fleury's expert critique on the MA crossover periods defined a fixed value, what are some of the other things? Would you say that the asset selection could also be biased?
Thank you for your teachings!
Vladimir
Guy Fleury,
I am aware of your incredible ability to send any profitable strategy into the sky since we
discussed Andreas Clenow's momentum in 2017.
It's no secret, but simple math, that if you use 3x tools and apply a 1.4 leverage to
a strategy with 30% CAGR you should get much more than 150% CAGR.
You got 89.86% CAGR because these 3x instruments only started trading in 2010.
The decision to use 3x tools and a leverage greater than 1.0 is usually made
at the last stage of development strategies.
Before that, we must use 1x tools and use 1.0 leverage for compatibility of results.
I can also see that you are starting to improve the strategy in the right direction by
cutting half the sources.
Hopefully I can see results where you apply pressure points and other tools from your
toolbox.
T Smith
Hi Guy Fleury
Great to see you in the discussion,. I have played with using just dollar as our signal, and have decrease sharpe considerably. whilst bring beta to 0.5.
T Smith
Vladimir totally agree. Best to build an algo with no leverage and just look to improving sharpe ratio. Leveraging later is then an option. Have you had much of a look into the concept of requiing more than 1 signal to exit the market?
Vladimir
T. Smith,
I don't have a solution right now, but I definitely want to either change the decision logic or reduce the number of sources.
Two months ago, this algorithm had only 4-6 sources and much fewer parameters.
I have played with using just dollar as our signal.
USD in this strategy has a negative sign.
mom[self.UUP] = mom[self.UUP] * (-1)
Guy Fleury
@Vladimir, your version of this trading strategy was already tested with 1.0x leverage. My task is to find out if it can scale up and do more, including to see if it can support leveraging in order to find the strategy's operational limits. Something I need to know before the end of the testing process. Otherwise, I will be wasting my time if those limits are found nearby, later, or much worse, after going live.
The way we test trading strategies, they have to generate all their profits from within, no matter what trading methods we use. There are no external funds added during a simulation. The tools we have are part of better stock selection, better timing, better gaming, better process amplification, better trade modulation, better protective measures, etc...
Leverage can be used over selected time periods where you anticipate for whatever reason that it will increase the potential return on your trade. It is like saying: you do not use it all the time, but can use it where you think it might count.
Nonetheless, it is a CAGR game, and basic math will matter. You can apply limited leveraging when your added alpha exceeds leveraging costs (here, the alpha being the excess return over the market average).
You want to know the strategy's limits before your “feasibility” simulation study ends. I have looked at many strategies that do not pass these basic tests. I usually lose interest quite rapidly and expedite their journey to file 13.
Here is a fun observation. After flatlining 3 of the signal components, we are left with a minus UUP. The thing is that if you reverse this signal to +UUP you should see the strategy lose a lot of money. But, if you do this, you get about the same profits, within 0.5% of each other on my 1 million initial capital scenario. Putting into question the “real” value of that signal? Note that you still get a little more with the negative signal (-UUP).
What kind of signal, that it be negative or its reverse, can produce about the same results? I would venture one that does not matter which side it is on.
How about flatlining UUP making the strategy having no switching signal, no trend definition? What would be the result? Such a move reduced overall profits by almost half (-49%). But still managed a +91.25% CAGR compared to the 98.73% and the 98.49% CAGR in -UUP and +UUP respectively. Therefore, UUP's presence was worthwhile, while its predictive powers were nil since you made about as much that UUP declared the trend up or down.
Vladimir
Guy Fleury,
This strategy yields good results, but I don't like just one word in the code in line 124,
and that word is any()
if (extreme[self.SIGNALS + self.PAIR_LIST]).any():
Just imagine that you are the president of the United States.
You invited the top twelve generals of the country to help you make decisions whether to start
a war with country x or not.
Eleven generals said no, but one said yes.
What will be your decisions?
Vladimir
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